---
title: "Big Cities Health Inventory Data Visualization"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(rio)
library(colorblindr)
library(janitor)
library(magrittr)
library(ggrepel)
library(fontawesome)
```

# Background {data-orientation=rows data-icon="fa-info-circle"}

Sidebar {.sidebar}
-------------------------------
**Background**

Welcome to my final project for EDLD610: Communicating and Transforming Data, an amazing course taught by [Daniel Anderson](https://twitter.com/datalorax_){target="_blank"} at the University of Oregon as part of a [Data Science Specialization](https://github.com/uo-datasci-specialization){target="_blank"}. The goal of this project is to provide three data visualizations using an open-source dataset and to document how these visualizations unfolded over different iterations. 

For my project, I used data from the [Big Cities Health Coalition](https://twitter.com/bigcitieshealth?lang=en){target="_blank"} (BCHC). The BCHC is a large-scale collaboration among 30 of the largest urban health departments in the United States. See the BCHC's [informational brochure](https://static1.squarespace.com/static/534b4cdde4b095a3fb0cae21/t/5c7fc5cd6e9a7f44b5abf311/1551877582500/BCHC_ABOUT+US.pdf){target="_blank"} for more details. You can download the complete dataset [here](http://bchi.bigcitieshealth.org/rails/active_storage/blobs/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBGdz09IiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--c6b5c30fbd8b79859797e1dc260a06064c8f3864/Current%20BCHI%20Platform%20Dataset%20(7-18)%20-%20Updated%20BCHI%20Platform%20Dataset%20-%20BCHI,%20Phase%20I%20&%20II.csv?disposition=attachment), which contains over 30,000 data points across a large variety of health indicators, e.g., behavioral health & substance abuse, chronic disease, environmental health, and life expectancy, to name just a few.  

This project includes only a tiny fraction of the available BCHC data, focusing in particular on **obesity rate**, **heart disease mortality rate**, and **opioid-related mortality rate**. Click on the icons to the right for more information on these variables. You can also view the source code for each of these plots by clicking [here][Source Code] or the top right of the screen. 

Row {data-height=600}
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### Title {.no-title}


Click the image below to access the BCHC data platform:

[![](bchc_logo_big.png)](http://www.bigcitieshealth.org/city-data){target="_blank"}


### Title {.no-title}


Cities included in the BCHC. Click the map below for more information on city membership. 

[![](cities_map.png)](http://www.bigcitieshealth.org/our-members-big-cities-health-coalition-bchc/){target="_blank"}


Row {data-height=90}
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### Title {.no-title}

**KEY VARIABLES OF INTEREST:**



Row {data-height=300}
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### Title {.no-title}

*Obesity Rate*

[![](obesity.png)](http://www.bigcitieshealth.org/obesity-physical-activity){target="_blank"}


### Title {.no-title}

*Heart Disease Mortality Rate*

[![](heart.png)](https://bchi.bigcitieshealth.org/indicators/1834/searches/22955){target="_blank"}


### Title {.no-title}

*Opioid-Related Mortality Rate*

[![](opioid.png)](http://www.bigcitieshealth.org/combatting-opioids){target="_blank"}



```{r import data, warning=FALSE}
data_raw <- import("http://bchi.bigcitieshealth.org/rails/active_storage/blobs/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBGdz09IiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--c6b5c30fbd8b79859797e1dc260a06064c8f3864/Current%20BCHI%20Platform%20Dataset%20(7-18)%20-%20Updated%20BCHI%20Platform%20Dataset%20-%20BCHI,%20Phase%20I%20&%20II.csv?disposition=attachment")

# wrangle data
data_filt <- data_raw %>% 
  clean_names() %>% 
  select(shortened_indicator_name, year, sex, race_ethnicity, value, place) %>% 
  filter(shortened_indicator_name %in% c("Adult Obesity","Heart Disease Mortality Rate","Race/Ethnicity", "Sex", "Opioid-related Overdose Mortality Rate")) %>% 
  mutate(value = as.numeric(value)) %>% 
  mutate_at(c("sex", "race_ethnicity", "place"), factor) %>% 
 mutate(place = plyr::mapvalues(x = .$place, from = c("Fort Worth (Tarrant County), TX", "Indianapolis (Marion County), IN", "Las Vegas (Clark County), NV", "Miami (Miami-Dade County), FL", "Oakland (Alameda County), CA", "Portland (Multnomah County), OR"), to = c("Fort Worth, TX", "Indianapolis, IN", "Las Vegas, NV", "Miami, FL", "Oakland, CA", "Portland, OR"))) %>% 
  na.omit()
```


# Obesity x City {data-icon="fa-weight"}

Sidebar {.sidebar}
-------------------------------
**Visualization #1**

This plot represents average obesity rates for adults (18 years and over) across all years in the dataset (2010-2018) for each city. Here, obesity rate refers to the percentage of the population that meets criteria for obesity. In general, obesity is defined in this dataset as [Body Mass Index (BMI)](https://www.cdc.gov/healthyweight/assessing/bmi/index.html){target="_blank"} of 30 or greater. This plot includes data for all races and genders. The average obesity rate for the entire U.S. is represented by the black bar. States that have higher obesity rates than the national average are colored red, and states below the national average are colored blue. From this plot, it is easy to discern that Detroit, MI had the highest average obesity rate from 2010-2018, while San Francisco, CA had the lowest average obesity rate during this time frame.

This plot is intended for a general audience. See the plots on the right for different iterations of this visualization.

```{r, warning}
# wrangle data
data_obesity <- data_filt %>% 
  filter(shortened_indicator_name == "Adult Obesity",
         sex == "Both",
         race_ethnicity == "All") %>% 
  spread(shortened_indicator_name, value) %>% 
  group_by(place) %>% 
  summarise(avg_obesity = mean(`Adult Obesity`, na.rm = TRUE),
            sd_obesity = sd(`Adult Obesity`),
                            n = n()) %>% 
  mutate(se_obesity = sd_obesity/(sqrt(n)))
```
 

Column {data-width=650}
-----------------------------------------------------------------------

### Final plot

```{r}
data_obesity %>% 
  mutate(compare_us_tot = ifelse(
    avg_obesity > .$avg_obesity[which(data_obesity$place == "U.S. Total")], "above",
    ifelse(avg_obesity < .$avg_obesity[which(data_obesity$place == "U.S. Total")], "below", "avg"))) %>% 
  ggplot(aes(fct_reorder(place, avg_obesity), avg_obesity)) + 
  geom_col(aes(fill = compare_us_tot), alpha = 0.8) +
  coord_flip() +
  scale_y_continuous(labels = scales::percent_format(scale = 1)) + 
  scale_fill_manual(values = c("#BA4A00", "black", "#ABCFF7")) +
  labs(title = "Average Obesity Rates per City", subtitle = "Data from 2010-2018", y = "Percent of Adults Who Are Obese", x = NULL, caption = "Vertical line represents the U.S. average.\n States above/below the U.S. average are colored red/blue, respectively.") + 
  theme_minimal() + 
  geom_hline(yintercept = data_obesity$avg_obesity[which(data_obesity$place == "U.S. Total")], linetype = 2) + 
  theme(legend.position = "none",
        panel.grid.major.y = element_blank())
```

Column {.tabset data-width=350}
-----------------------------------------------------------------------

### Version 1

```{r}
data_obesity %>% 
  ggplot(aes(place, avg_obesity, avg_obesity)) + 
  geom_col() +
  coord_flip()
```

> Here is the first pass at the plot. Re-ordering the bars in descending order would make it much easier to visually identify which states have the highest/lowest obesity rates. The graph also needs a title and a better X axis label. We can also remove the `place` label since it's not really necessary.

### Version 2

```{r}
data_obesity %>% 
  ggplot(aes(fct_reorder(place, avg_obesity), avg_obesity)) + 
  geom_col() + 
  coord_flip() +
  scale_y_continuous(labels = scales::percent_format(scale = 1)) + 
  labs(title = "Percent of Adults Who Are Obese", y = "Percent", x = NULL) + 
  theme_minimal()
```

> Now that the bars are re-ordered, we can easily identify Detroit as the city with the higehst average obesity rates and San Francisco as the city with the lowest. Another way to improve the graph would be to make the bar for the U.S. average stand out and color the bars for individual cities according to whether they are above or below the U.S. average. 

### Version 3

```{r}
data_obesity %>% 
  mutate(compare_us_tot = ifelse(
    avg_obesity > .$avg_obesity[which(data_obesity$place == "U.S. Total")], "above",
    ifelse(avg_obesity < .$avg_obesity[which(data_obesity$place == "U.S. Total")], "below", "avg"))) %>% 
  ggplot(aes(fct_reorder(place, avg_obesity), avg_obesity)) + 
  geom_segment(aes(color = compare_us_tot, x = fct_reorder(place, avg_obesity), xend = place, y=0, yend = avg_obesity), size = 1, alpha = 0.7) +
  geom_point(aes(color = compare_us_tot), size = 3, alpha = 0.7) +
  coord_flip() +
  scale_y_continuous(labels = scales::percent_format(scale = 1)) + 
  scale_color_manual(values = c("#BA4A00", "black", "#ABCFF7")) +
  labs(title = "Percent of Adults Who Are Obese", y = "Percent", x = NULL) + 
  theme_minimal() + 
  geom_hline(yintercept = data_obesity$avg_obesity[which(data_obesity$place == "U.S. Total")], linetype = 2) + 
  theme(legend.position = "none")
```

> Now we have a simple color scheme that quickly tells us whether a given city is above or below the U.S. average in terms of its obesity rates. Adding a vertical line at the U.S. average also makes it easier to visually compare *how much* each city is above/below the national average. Here I'm also trying out a lollipop plot just for fun. Ultimately, I think a bar plot looks cleaner and makes the colors easier to discern. We can also make the title and axis label more informative and add a caption to explain the color coding. Lastly, we can remove the horizonal grid lines, as they don't add any utility to the visualization and cause unnecessary visual "clutter."

# Heart Disease x Obesity {data-icon="fa-heartbeat"}

Sidebar {.sidebar}
-------------------------------
**Visualization #2**

This plot shows the relationship between average adult obesity rates and average heart disease mortality rates at the city level, collapsed across all years available in the dataset (2010-2018). Again, obesity rate refers to the percentage of adults with BMI ≥30. Heart disease mortality rate refers to the number of individuals per 100,000 who have died from heart disease. This variable is age-adjusted, likely to account for the fact that risk for heart complications such as heart attack, stroke or coronary heart disease [increases with age](https://www.nia.nih.gov/health/heart-health-and-aging){target="_blank"}. This plot includes data for all races and genders. From this graph, we can see that cities with higher obesity rates tend to also have higher heart disease mortality rates. This is not at all surprising, given the evidence that [obesity increases the risk for cardiovascular disease](https://www.obesityaction.org/community/article-library/cardiovascular-disease-obesity-and-the-heart/){target="_blank"}. 

This plot is intended for more of a scientific audience, as it communicates a statistical relationship between two variables. See the plots on the right for different iterations of this visualization. 

```{r}
# wrangle data
obesity_hdmr <- data_filt %>%
  filter(shortened_indicator_name %in% c("Adult Obesity", "Heart Disease Mortality Rate"),
         sex == "Both",
         race_ethnicity == "All",
         place != "U.S. Total") %>%
  mutate(i = row_number()) %>%
  spread(shortened_indicator_name, value) %>%
  group_by(place) %>%
  summarize(avg_obesity =  mean(`Adult Obesity`, na.rm = TRUE),
            avg_hdmr = mean(`Heart Disease Mortality Rate`, na.rm = TRUE))
```

Column {data-width=650}
-----------------------------------------------------------------------

### Final plot

```{r}
## 3 most obese cities
top_3_obese <- obesity_hdmr %>% 
  top_n(3, avg_obesity)

## 3 least obese cities
bottom_3_obese <- obesity_hdmr %>% 
  top_n(-3, avg_obesity)

obesity_hdmr %>% 
  ggplot(aes(avg_obesity, avg_hdmr)) + 
  geom_point(size = 5, alpha = 0.5, color = "gray70") +
  geom_point(data = top_3_obese, size = 5, color = "#BA4A00", alpha = 0.7) +
  geom_point(data = bottom_3_obese, size = 5, color = "#ABCFF7", alpha= 0.7) +
  geom_smooth(method = "lm", alpha = 0.2, color = "gray60") +
  geom_text_repel(data = top_3_obese, aes(label = place), min.segment.length = 0) +
  geom_text_repel(data = bottom_3_obese, aes(label = place), min.segment.length = 0) +
  theme_minimal() + 
  scale_x_continuous(labels = scales::percent_format(scale = 1)) + 
  labs(x = "Percent of Adults Who Are Obese", y = "Heart Disease Mortality Rate", title = "Relationship between Obesity and Heart Disease", subtitle = "Data from 2010-2018", caption = "3 most/least obese states are labeled and colored red/blue, respectively.\nHeart disease mortality rates age adjusted; per 100,000 people.")
```


Column {.tabset data-width=350}
-----------------------------------------------------------------------

### Version 1

```{r}
obesity_hdmr %>% 
  ggplot(aes(avg_obesity, avg_hdmr)) + 
  geom_point() +
  geom_smooth(method = "lm")
```

### Version 2

```{r}
obesity_hdmr %>% 
  ggplot(aes(avg_obesity, avg_hdmr)) + 
  geom_point() +
  geom_smooth(method = "lm") +
  geom_text_repel(aes(label = place)) + 
  theme_minimal()
```

### Version 3

```{r}
obesity_hdmr %>% 
  ggplot(aes(avg_obesity, avg_hdmr)) + 
  geom_point() +
  geom_smooth(method = "lm") +
  geom_text_repel(data = top_3_obese, aes(label = place), min.segment.length = 0) +
  geom_text_repel(data = bottom_3_obese, aes(label = place), min.segment.length = 0) +
  theme_minimal()
```


# Opioid Deaths x Gender {data-icon="fa-tablets"}

Sidebar {.sidebar}
-------------------------------
**Visualization #3**

See the plots on the right for different iterations of this visualization. 

```{r}
# wrangle data
data_opioid <- data_raw %>% 
  clean_names() %>% 
  select(shortened_indicator_name, year, sex, race_ethnicity, value, place) %>% 
  filter(shortened_indicator_name %in% c("Opioid-Related Overdose Mortality Rate")) %>% 
  mutate(value = as.numeric(value)) %>% 
  mutate_at(c("sex", "race_ethnicity", "place"), factor) %>% 
  na.omit()

# identify city with highest opioid-related overdose mortality rate from 2010 to 2016
top_opioid = data_opioid %>% 
  filter(sex == "Both",
         race_ethnicity == "All",
         place != "U.S. Total",
         year %in% 2010:2016) %>% 
  unique() %>% 
  spread(shortened_indicator_name, value) %>% 
  group_by(place) %>% 
  summarize(mean_opioid = mean(`Opioid-Related Overdose Mortality Rate`, na.rm = TRUE)) %>% 
  top_n(1) %>% 
  select(place)
```

Column {data-width=650}
-----------------------------------------------------------------------

### Final plot

```{r}
data_opioid %>% 
  filter(sex != "Both", 
         race_ethnicity == "All",
         place == top_opioid$place,
         year %in% 2010:2016) %>% 
  spread(shortened_indicator_name, value) %>% 
  ggplot(aes(year, `Opioid-Related Overdose Mortality Rate`, color= sex)) + 
  geom_line(size= 2) +
  geom_point(size = 4) + 
  labs(x = NULL, y = "Opioid-Related Overdose Mortality Rate", title = "Opioid-use Related Mortality Rates Over Time", subtitle = "Colombus, OH", caption = "Rates are age-adjusted per 100,000 people.") + 
  theme_minimal() + 
  scale_color_OkabeIto() +
  theme(legend.position = "none") +
  geom_label(data = data_opioid %>% 
  filter(sex != "Both", 
         race_ethnicity == "All",
         place == top_opioid$place,
         year == 2016) %>% 
         spread(shortened_indicator_name, value), 
         aes(y =`Opioid-Related Overdose Mortality Rate`, label = sex),
         nudge_x = -0.7,
         size = 5) 
```

Column {.tabset data-width=350}
-----------------------------------------------------------------------

### Version 1

```{r}
data_opioid %>% 
  filter(sex != "Both", 
         race_ethnicity == "All",
         place == top_opioid$place,
         year %in% 2010:2016) %>% 
  spread(shortened_indicator_name, value) %>% 
  ggplot(aes(year, `Opioid-Related Overdose Mortality Rate`, color= sex)) + 
  geom_line() 
```


### Version 2

```{r}
data_opioid %>% 
  filter(sex != "Both", 
         race_ethnicity == "All",
         place == top_opioid$place,
         year %in% 2010:2016) %>% 
  spread(shortened_indicator_name, value) %>% 
  ggplot(aes(year, `Opioid-Related Overdose Mortality Rate`, color= sex)) + 
  geom_line(size= 2) +
  geom_point(size = 4) + 
  labs(x = NULL, y = "Opioid-Related Overdose Mortality Rate", title = "Opioid-use Related Mortality Rates Over Time", subtitle = "Colombus, OH", caption = "Rates are age-adjusted per 100,000 people.") + 
  theme_minimal() 
```